1. Field of the Invention
The present invention relates generally to a semi-automatic technique that can reconstruct 3-D building models by using building outline segments, specifically, a technique that can establish the topology between separated line segments. The present invention further relates to a technique for performing 3-D mapping by integrating the digital photogrammetric mapping with 3-D building model reconstruction. The range of application for the generated 3-D building model is very broad. In addition to urban planning, it is useful for cellular phone station setups, flight simulations, virtual reality, noise or air pollution simulations, environmental impact evaluations, environmental monitoring, change detection, and various other 3-D geographical information applications.
2. Description of the Related Art
In digital photogrammetric mapping, a digital photogrammetric system and digital aerial stereo-images (FIG. 1) are used to perform manual stereo-measurement on ground objects. The delineation of building outlines is performed by the measurement of each corner points. Two consecutive measurements form a building outline segment. If a building's corner is occluded, then manual inference or on-site surveying is necessary to complete the measurement of occluded corner. Finally, in the produced topographic map, the height about the building is denoted by number of stories. The whole procedure is very laborious, time consuming, and costly
On the other hand, in the generation of 3-D building models, it is also performed manually on a digital photogrammetric system using aerial stereo-images. FIG. 3 illustrates an abstract flow chart of a fully manual 3-D building model reconstruction. In step 300, the aerial stereo-images with known orientation parameters are used. In step 302, the building corners are manually measured by stereo-measurements. When the occlusion of the building corner occurs, manual inference or on-site surveying is necessary to complete the measurement of occluded corner. Finally, in step 304, manually structuring is performed on the measurements and the topology is thus established to construct the 3-D building model. Due to manually stereo-measurement errors, it always causes topologic errors at neighboring buildings and thus excess manual editing is necessary to deal with the connection problems. The whole procedure is also very laborious, time consuming and costly.
In heavily developed cities, the buildings are constructed with a very high density in a continuous morphology. This will introduce serious occlusion problems between neighboring buildings. When establishing the topology between two neighboring buildings, topologic errors are easily made due to manual measurement errors. Therefore, a significant amount of labor and cost will incur when the above fully manual reconstruction procedure is utilized.
One can realized that the above digital photogrammetric mapping and manual 3-D building model reconstruction have one common procedure. That is the manual stereo-measurements. Despite of the inevitable time spent on manual stereo-measurements, the majority of the workload is spent on the subsequent manual editing of the topomap and manual structuring of the building models. The purpose of the present invention is to reduce the workload of these manual operations, such as the manual stereo-measurements, manual editing of buildings in photogrammetric mapping, and the manual structuring of building models.
The relevant literatures with respect to 3-D building model reconstruction can be categorized into the fully automatic and the semi-automatic strategies. Their purpose is also aimed at reducing the workload of an operator and thus to reduce the production cost.
In heavily developed cities, buildings are densely constructed in a continuous morphology. Accordingly, serious occlusion problems between neighboring buildings are resulted. When establishing the topology between two neighboring buildings, topologic errors are easily made due to manual measurement errors. Therefore, a significant amount of labor and cost will incur when the above fully manual reconstruction procedure is utilized.
FIG. 4 illustrates an abstract flow chart of a fully automatic strategy. In step 400, aerial stereo-images with known orientation parameters are utilized. In steps 402 and 404, the feature extraction and feature matching are performed, respectively, to obtain roof-edges or building corners information. In steps 406, 408, and 410, a building model hypothesis was further generated, tested, and verified, respectively The final 3-D building model may thus be obtained.
For example, Fischer et al. (1998) start from feature extraction and feature matching using multi-view stereo-images. The 3-D building corners and roof-edges are derived and inferred as building parts. A building hypothesis was then generated and verified by mutual interactions between the 2-D and 3-D processes. Henrisson (1998) also starts from image feature extraction and matching, to obtain 3-D building outline segments. Color attributes along the extracted line segments are calculated and applied for similarity grouping, and then incorporating into coplanar grouping to infer a 3-D building model.
The above two authors use automatic image matching technique to obtain 3-D information. In high-resolution aerial stereo-images, mismatching may happened due to ambiguity problems, building occlusion problems, shadow effects, and poor image quality. All can result in incorrect or incomplete 3-D information, which will further affect the reliability and accuracy of the generated building models. As shown in FIG. 5, the difference in the areas of frames 500 and 502 is small, but is easy and differentiable by the human eye. However, in an automatic image matching process it relies on the features within the frames. Those areas with similar image feature may introduce incorrect matching results. That is the reason why a fully automatic strategy has not been applicable up to date.
On the other hand, the semi-automatic strategy is also adopted in various approaches. Some of them follow the flow chart as shown in FIG. 6. In step 600, aerial images with known orientation parameters are utilized. In step 602, manual stereo-measurements are performed. In step 604, an automatic building model reconstruction is performed. In step 606, manual visual inspections are performed to complete the modeling. Gülch et al. (Gülch, E., H. Muller, T. Läbe & L. Ragia, 1998. On the performance of semi-automatic building extraction, Proceedings of ISPRS Commission III Symposium, Columbus, Ohio, Jul. 6–10, 1998) and Grün & Wang (Gün, A. and X. Wang, 1998, CC-Modeler: A Topology Generator for 3-D Building Models, IJPRS, Vol. 53, pp. 286–295.) are two examples of this approach. Commercial software that applies the techniques proposed in the above two approaches has already been released up to date.
In contrast to point-based measurement in traditional photogrammetry, Gülch et al. (1998) proposed a building primitive-based measurement. In their approach, the operator is responsible for choosing an appropriate building primitive, which is selected from a predefined building model database. In the modeling phase, the selected building primitive was back-project onto one aerial image (as shown in FIG. 7a) via monoscopic viewing, as shown by the wire-frame model in FIG. 7b. The operator has to adjust the wire-frame model to fit the corresponding building boundary (as shown in FIG. 7c) by using three possible strategies: (1) a purely manual adaption, (2) a guided adaption, or (3) an automated adaption. A complex building is decomposed into some basic building types and constructed using a Constructive Solid Geometry (CSG) tree. The operator is also responsible for handling the CSG tree structures. Although the approach is innovative, the operator takes too heavy responsibility, necessitating a qualified operator. The approach may be efficient for simple structure and specific type of building, but not for a complex structure building and a group of connected buildings especially in densely built-up areas where occlusions and shadows frequently occur.
The above two authors use automatic image matching technique to obtain 3-D information. In high-resolution aerial stereo-images, mismatching may happen due to ambiguity problems, building occlusion problems, shadow effects, and poor image quality. All can result in incorrect or incomplete 3-D information, which will further affect the reliability and accuracy of the generated building models. As shown in FIG. 5, the difference in the areas of frames 500 and 502 is small, but is easy and differentiable by the human eye. However, in an automatic image matching process it relies on the features within the frames. Those areas with similar image feature may introduce incorrect matching results. That is the reason why a fully automatic strategy has not been applicable up to date.